In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.
Legal claims defining the scope of protection, as filed with the USPTO.
12. The electronic device of claim 1, wherein the gated recurrent unit is a minimal gated recurrent unit.
13. The electronic device of claim 1, wherein the gated recurrent unit is spatially recurrent.
14. The electronic device of claim 1, wherein the convolutional neural network is a separable convolutional neural network.
15. The electronic device of claim 1, wherein the software further causes the electronic device to determine one or more touches using the third touch data outputted from the convolutional neural network.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
March 12, 2021
March 7, 2023
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.